At Fujitsu, our purpose is to make the world more sustainable by building trust in society through innovation. Founded in Japan in 1935, Fujitsu has been a pioneer in technology and innovation for decades. Today, as a world-leading digital transformation partner, we are committed to transforming business and society in the digital age.

With approximately 130,000 employees across over 50 countries, Fujitsu offers a broad range of products, services, and solutions. We collaborate with our customers to co-create solutions that drive enterprise-wide digitalization while actively working to address social issues and contribute to the United Nations Sustainable Development Goals (SDGs).

MY MISSION : 

As an AI Engineer, your mission is to design, build, and deploy AI-powered applications that solve concrete business problems. Working with the AI Services Team Lead, architects, and business analysts, you will translate business requirements into reliable solutions using large language models, RAG, and agentic workflows.

You will contribute throughout the development lifecycle, from rapid prototyping and technical validation to production deployment and continuous improvement.

MY RESPONSIBILITIES : 

• AI Application Development: Build backend services and REST APIs using Python and FastAPI, with asynchronous processing, robust error handling, and integration with business systems.
• Agentic Workflows and Orchestration: Implement LangGraph and LangChain workflows covering state management, routing, tools, memory, retries, and human-in-the-loop interactions.
• LLM Integration: Integrate models through Azure OpenAI, OpenAI, Mistral, or IBM watsonx, managing structured outputs, latency, costs, and service failures.
• RAG Development: Build document ingestion and retrieval pipelines covering extraction, chunking, embeddings, vector databases, hybrid search, reranking, and source citations.
• Quality and Evaluation: Write automated tests and evaluate retrieval quality, generated responses, and agent behaviour. Use LangSmith or equivalent tools to trace workflows and investigate failures.
• Production Engineering: Package applications with Docker, contribute to CI/CD pipelines, and support cloud deployment. Apply secure development practices, including authentication, access control, and secrets management.
• Rapid Prototyping and AI-Assisted Development: Use vibe coding and AI-assisted development tools to accelerate prototyping and implementation, taking responsibility for reviewing, testing, securing, and maintaining the resulting code.
• Responsible AI: Implement privacy, transparency, traceability, and human oversight measures in line with project requirements, working with security and compliance specialists.
• Collaboration: Document technical decisions and contribute to solution estimates, feasibility assessments, and demonstrations.
  
  

Required technical skills :

• Strong Python and asynchronous programming: Practical experience with async/await, asyncio, concurrent API calls, timeouts, retries, cancellation, and task management.
• FastAPI and REST APIs: API design, input validation, authentication, and consistent error handling.
• LangGraph: States, nodes, routing, persistence, memory, failure recovery, and human-in-the-loop workflows.
• LangChain: Tool integration, prompting, structured outputs, and LLM integration.
• LLM integration: Hands-on experience with Azure OpenAI/OpenAI or equivalent providers, including rate limits, latency, and cost management.
• RAG systems: Experience with ingestion, chunking, embeddings, vector databases, hybrid search, reranking, citations, and retrieval evaluation.
• Multi-agent architecture: Experience implementing agent coordination, delegation, shared context, and failure handling.
• MCP (Model Context Protocol): Hands-on experience connecting agents to business tools and implementing or integrating MCP clients and servers.
• A2A (Agent2Agent Protocol): Hands-on experience integrating external agents, exchanging messages, and managing task lifecycles and errors.
• Observability and evaluation: Experience with LangSmith or equivalent tools for tracing and assessing LLM applications and agent workflows.
• Vibe coding and AI-assisted development: Practical experience with tools such as Claude Code, GitHub Copilot, or Codex, including providing clear instructions, reviewing generated changes, and validating results through tests.
• Software engineering: Git, Docker, CI/CD, and Python testing with pytest, including unit, integration, asynchronous tests, and mocks.
• Cloud: Experience deploying applications to a cloud platform, ideally Microsoft Azure.
• AI governance and regulatory awareness: Familiarity with the EU AI Act and GDPR, including their practical implications for data privacy, human oversight, transparency, traceability, and responsible AI development.
  
  

CERTIFICATIONS

The following constitute an advantage:

• Microsoft AI certifications: Particularly those focused on Azure AI, generative AI, and agentic solutions.
• Claude certifications: Particularly those focused on architecture, Claude Code, and agent development.
• Other relevant certifications: Cloud engineering, AI engineering, AI governance, or multi-agent architecture.

SOFT SKILLS :

• Ability to translate business requirements into clear technical tasks.
• Autonomy, reliability, and ownership of development work.
• Clear communication with engineers, architects, and business analysts.
• A practical approach to problem-solving, with attention to quality and maintainability.
• Willingness to learn, share knowledge, and adapt as AI technologies evolve.

LANGUAGES : 

• French and English: Professional proficiency in both languages, written and spoken, to collaborate with clients and colleagues, participate in technical discussions, and produce clear documentation.